diff --git "a/pipeline.py" "b/pipeline.py"
--- "a/pipeline.py"
+++ "b/pipeline.py"
@@ -1,79 +1,52 @@
-# source https://github.com/huggingface/diffusers/blob/main/examples/community/lpw_stable_diffusion_onnx.py
+# source https://github.com/huggingface/diffusers/blob/main/examples/community/lpw_stable_diffusion_xl.py
+## ----------------------------------------------------------
+# A SDXL pipeline can take unlimited weighted prompt
+#
+# Author: Andrew Zhu
+# Github: https://github.com/xhinker
+# Medium: https://medium.com/@xhinker
+## -----------------------------------------------------------
+
import inspect
-import re
-from typing import Callable, List, Optional, Union
+import os
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-import numpy as np
-import PIL.Image
import torch
-from packaging import version
-from transformers import CLIPImageProcessor, CLIPTokenizer
-
-import diffusers
-from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, SchedulerMixin
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.utils import logging
-
-
-try:
- from diffusers.pipelines.onnx_utils import ORT_TO_NP_TYPE
-except ImportError:
- ORT_TO_NP_TYPE = {
- "tensor(bool)": np.bool_,
- "tensor(int8)": np.int8,
- "tensor(uint8)": np.uint8,
- "tensor(int16)": np.int16,
- "tensor(uint16)": np.uint16,
- "tensor(int32)": np.int32,
- "tensor(uint32)": np.uint32,
- "tensor(int64)": np.int64,
- "tensor(uint64)": np.uint64,
- "tensor(float16)": np.float16,
- "tensor(float)": np.float32,
- "tensor(double)": np.float64,
- }
-
-try:
- from diffusers.utils import PIL_INTERPOLATION
-except ImportError:
- if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
- PIL_INTERPOLATION = {
- "linear": PIL.Image.Resampling.BILINEAR,
- "bilinear": PIL.Image.Resampling.BILINEAR,
- "bicubic": PIL.Image.Resampling.BICUBIC,
- "lanczos": PIL.Image.Resampling.LANCZOS,
- "nearest": PIL.Image.Resampling.NEAREST,
- }
- else:
- PIL_INTERPOLATION = {
- "linear": PIL.Image.LINEAR,
- "bilinear": PIL.Image.BILINEAR,
- "bicubic": PIL.Image.BICUBIC,
- "lanczos": PIL.Image.LANCZOS,
- "nearest": PIL.Image.NEAREST,
- }
-# ------------------------------------------------------------------------------
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
+from PIL import Image
+from transformers import (
+ CLIPImageProcessor,
+ CLIPTextModel,
+ CLIPTextModelWithProjection,
+ CLIPTokenizer,
+ CLIPVisionModelWithProjection,
+)
-re_attention = re.compile(
- r"""
-\\\(|
-\\\)|
-\\\[|
-\\]|
-\\\\|
-\\|
-\(|
-\[|
-:([+-]?[.\d]+)\)|
-\)|
-]|
-[^\\()\[\]:]+|
-:
-""",
- re.X,
+from diffusers import DiffusionPipeline, StableDiffusionXLPipeline
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
+from diffusers.models.attention_processor import (
+ AttnProcessor2_0,
+ FusedAttnProcessor2_0,
+ LoRAAttnProcessor2_0,
+ LoRAXFormersAttnProcessor,
+ XFormersAttnProcessor,
+)
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import (
+ deprecate,
+ is_accelerate_available,
+ is_accelerate_version,
+ is_invisible_watermark_available,
+ logging,
+ replace_example_docstring,
)
+from diffusers.utils.torch_utils import randn_tensor
+
+
+if is_invisible_watermark_available():
+ from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
def parse_prompt_attention(text):
@@ -89,6 +62,7 @@ def parse_prompt_attention(text):
\\] - literal character ']'
\\ - literal character '\'
anything else - just text
+
>>> parse_prompt_attention('normal text')
[['normal text', 1.0]]
>>> parse_prompt_attention('an (important) word')
@@ -110,6 +84,17 @@ def parse_prompt_attention(text):
['sky', 1.4641000000000006],
['.', 1.1]]
"""
+ import re
+
+ re_attention = re.compile(
+ r"""
+ \\\(|\\\)|\\\[|\\]|\\\\|\\|\(|\[|:([+-]?[.\d]+)\)|
+ \)|]|[^\\()\[\]:]+|:
+ """,
+ re.X,
+ )
+
+ re_break = re.compile(r"\s*\bBREAK\b\s*", re.S)
res = []
round_brackets = []
@@ -139,7 +124,11 @@ def parse_prompt_attention(text):
elif text == "]" and len(square_brackets) > 0:
multiply_range(square_brackets.pop(), square_bracket_multiplier)
else:
- res.append([text, 1.0])
+ parts = re.split(re_break, text)
+ for i, part in enumerate(parts):
+ if i > 0:
+ res.append(["BREAK", -1])
+ res.append([part, 1.0])
for pos in round_brackets:
multiply_range(pos, round_bracket_multiplier)
@@ -162,988 +151,2170 @@ def parse_prompt_attention(text):
return res
-def get_prompts_with_weights(pipe, prompt: List[str], max_length: int):
- r"""
- Tokenize a list of prompts and return its tokens with weights of each token.
-
- No padding, starting or ending token is included.
+def get_prompts_tokens_with_weights(clip_tokenizer: CLIPTokenizer, prompt: str):
"""
- tokens = []
- weights = []
- truncated = False
- for text in prompt:
- texts_and_weights = parse_prompt_attention(text)
- text_token = []
- text_weight = []
- for word, weight in texts_and_weights:
- # tokenize and discard the starting and the ending token
- token = pipe.tokenizer(word, return_tensors="np").input_ids[0, 1:-1]
- text_token += list(token)
- # copy the weight by length of token
- text_weight += [weight] * len(token)
- # stop if the text is too long (longer than truncation limit)
- if len(text_token) > max_length:
- truncated = True
- break
- # truncate
- if len(text_token) > max_length:
- truncated = True
- text_token = text_token[:max_length]
- text_weight = text_weight[:max_length]
- tokens.append(text_token)
- weights.append(text_weight)
- if truncated:
- logger.warning("Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples")
- return tokens, weights
-
-
-def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad, no_boseos_middle=True, chunk_length=77):
- r"""
- Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
+ Get prompt token ids and weights, this function works for both prompt and negative prompt
+
+ Args:
+ pipe (CLIPTokenizer)
+ A CLIPTokenizer
+ prompt (str)
+ A prompt string with weights
+
+ Returns:
+ text_tokens (list)
+ A list contains token ids
+ text_weight (list)
+ A list contains the correspodent weight of token ids
+
+ Example:
+ import torch
+ from transformers import CLIPTokenizer
+
+ clip_tokenizer = CLIPTokenizer.from_pretrained(
+ "stablediffusionapi/deliberate-v2"
+ , subfolder = "tokenizer"
+ , dtype = torch.float16
+ )
+
+ token_id_list, token_weight_list = get_prompts_tokens_with_weights(
+ clip_tokenizer = clip_tokenizer
+ ,prompt = "a (red:1.5) cat"*70
+ )
"""
- max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
- weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
- for i in range(len(tokens)):
- tokens[i] = [bos] + tokens[i] + [pad] * (max_length - 1 - len(tokens[i]) - 1) + [eos]
- if no_boseos_middle:
- weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i]))
- else:
- w = []
- if len(weights[i]) == 0:
- w = [1.0] * weights_length
- else:
- for j in range(max_embeddings_multiples):
- w.append(1.0) # weight for starting token in this chunk
- w += weights[i][j * (chunk_length - 2) : min(len(weights[i]), (j + 1) * (chunk_length - 2))]
- w.append(1.0) # weight for ending token in this chunk
- w += [1.0] * (weights_length - len(w))
- weights[i] = w[:]
+ texts_and_weights = parse_prompt_attention(prompt)
+ text_tokens, text_weights = [], []
+ for word, weight in texts_and_weights:
+ # tokenize and discard the starting and the ending token
+ token = clip_tokenizer(word, truncation=False).input_ids[1:-1] # so that tokenize whatever length prompt
+ # the returned token is a 1d list: [320, 1125, 539, 320]
+
+ # merge the new tokens to the all tokens holder: text_tokens
+ text_tokens = [*text_tokens, *token]
- return tokens, weights
+ # each token chunk will come with one weight, like ['red cat', 2.0]
+ # need to expand weight for each token.
+ chunk_weights = [weight] * len(token)
+ # append the weight back to the weight holder: text_weights
+ text_weights = [*text_weights, *chunk_weights]
+ return text_tokens, text_weights
-def get_unweighted_text_embeddings(
- pipe,
- text_input: np.array,
- chunk_length: int,
- no_boseos_middle: Optional[bool] = True,
+
+def group_tokens_and_weights(token_ids: list, weights: list, pad_last_block=False):
+ """
+ Produce tokens and weights in groups and pad the missing tokens
+
+ Args:
+ token_ids (list)
+ The token ids from tokenizer
+ weights (list)
+ The weights list from function get_prompts_tokens_with_weights
+ pad_last_block (bool)
+ Control if fill the last token list to 75 tokens with eos
+ Returns:
+ new_token_ids (2d list)
+ new_weights (2d list)
+
+ Example:
+ token_groups,weight_groups = group_tokens_and_weights(
+ token_ids = token_id_list
+ , weights = token_weight_list
+ )
+ """
+ bos, eos = 49406, 49407
+
+ # this will be a 2d list
+ new_token_ids = []
+ new_weights = []
+ while len(token_ids) >= 75:
+ # get the first 75 tokens
+ head_75_tokens = [token_ids.pop(0) for _ in range(75)]
+ head_75_weights = [weights.pop(0) for _ in range(75)]
+
+ # extract token ids and weights
+ temp_77_token_ids = [bos] + head_75_tokens + [eos]
+ temp_77_weights = [1.0] + head_75_weights + [1.0]
+
+ # add 77 token and weights chunk to the holder list
+ new_token_ids.append(temp_77_token_ids)
+ new_weights.append(temp_77_weights)
+
+ # padding the left
+ if len(token_ids) > 0:
+ padding_len = 75 - len(token_ids) if pad_last_block else 0
+
+ temp_77_token_ids = [bos] + token_ids + [eos] * padding_len + [eos]
+ new_token_ids.append(temp_77_token_ids)
+
+ temp_77_weights = [1.0] + weights + [1.0] * padding_len + [1.0]
+ new_weights.append(temp_77_weights)
+
+ return new_token_ids, new_weights
+
+
+def get_weighted_text_embeddings_sdxl(
+ pipe: StableDiffusionXLPipeline,
+ prompt: str = "",
+ prompt_2: str = None,
+ neg_prompt: str = "",
+ neg_prompt_2: str = None,
+ num_images_per_prompt: int = 1,
+ device: Optional[torch.device] = None,
+ clip_skip: Optional[int] = None,
):
"""
- When the length of tokens is a multiple of the capacity of the text encoder,
- it should be split into chunks and sent to the text encoder individually.
+ This function can process long prompt with weights, no length limitation
+ for Stable Diffusion XL
+
+ Args:
+ pipe (StableDiffusionPipeline)
+ prompt (str)
+ prompt_2 (str)
+ neg_prompt (str)
+ neg_prompt_2 (str)
+ num_images_per_prompt (int)
+ device (torch.device)
+ clip_skip (int)
+ Returns:
+ prompt_embeds (torch.Tensor)
+ neg_prompt_embeds (torch.Tensor)
"""
- max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
- if max_embeddings_multiples > 1:
- text_embeddings = []
- for i in range(max_embeddings_multiples):
- # extract the i-th chunk
- text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].copy()
-
- # cover the head and the tail by the starting and the ending tokens
- text_input_chunk[:, 0] = text_input[0, 0]
- text_input_chunk[:, -1] = text_input[0, -1]
-
- text_embedding = pipe.text_encoder(input_ids=text_input_chunk)[0]
-
- if no_boseos_middle:
- if i == 0:
- # discard the ending token
- text_embedding = text_embedding[:, :-1]
- elif i == max_embeddings_multiples - 1:
- # discard the starting token
- text_embedding = text_embedding[:, 1:]
- else:
- # discard both starting and ending tokens
- text_embedding = text_embedding[:, 1:-1]
+ device = device or pipe._execution_device
+
+ if prompt_2:
+ prompt = f"{prompt} {prompt_2}"
+
+ if neg_prompt_2:
+ neg_prompt = f"{neg_prompt} {neg_prompt_2}"
+
+ prompt_t1 = prompt_t2 = prompt
+ neg_prompt_t1 = neg_prompt_t2 = neg_prompt
+
+ if isinstance(pipe, TextualInversionLoaderMixin):
+ prompt_t1 = pipe.maybe_convert_prompt(prompt_t1, pipe.tokenizer)
+ neg_prompt_t1 = pipe.maybe_convert_prompt(neg_prompt_t1, pipe.tokenizer)
+ prompt_t2 = pipe.maybe_convert_prompt(prompt_t2, pipe.tokenizer_2)
+ neg_prompt_t2 = pipe.maybe_convert_prompt(neg_prompt_t2, pipe.tokenizer_2)
+
+ eos = pipe.tokenizer.eos_token_id
+
+ # tokenizer 1
+ prompt_tokens, prompt_weights = get_prompts_tokens_with_weights(pipe.tokenizer, prompt_t1)
+ neg_prompt_tokens, neg_prompt_weights = get_prompts_tokens_with_weights(pipe.tokenizer, neg_prompt_t1)
+
+ # tokenizer 2
+ prompt_tokens_2, prompt_weights_2 = get_prompts_tokens_with_weights(pipe.tokenizer_2, prompt_t2)
+ neg_prompt_tokens_2, neg_prompt_weights_2 = get_prompts_tokens_with_weights(pipe.tokenizer_2, neg_prompt_t2)
+
+ # padding the shorter one for prompt set 1
+ prompt_token_len = len(prompt_tokens)
+ neg_prompt_token_len = len(neg_prompt_tokens)
+
+ if prompt_token_len > neg_prompt_token_len:
+ # padding the neg_prompt with eos token
+ neg_prompt_tokens = neg_prompt_tokens + [eos] * abs(prompt_token_len - neg_prompt_token_len)
+ neg_prompt_weights = neg_prompt_weights + [1.0] * abs(prompt_token_len - neg_prompt_token_len)
+ else:
+ # padding the prompt
+ prompt_tokens = prompt_tokens + [eos] * abs(prompt_token_len - neg_prompt_token_len)
+ prompt_weights = prompt_weights + [1.0] * abs(prompt_token_len - neg_prompt_token_len)
+
+ # padding the shorter one for token set 2
+ prompt_token_len_2 = len(prompt_tokens_2)
+ neg_prompt_token_len_2 = len(neg_prompt_tokens_2)
+
+ if prompt_token_len_2 > neg_prompt_token_len_2:
+ # padding the neg_prompt with eos token
+ neg_prompt_tokens_2 = neg_prompt_tokens_2 + [eos] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
+ neg_prompt_weights_2 = neg_prompt_weights_2 + [1.0] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
+ else:
+ # padding the prompt
+ prompt_tokens_2 = prompt_tokens_2 + [eos] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
+ prompt_weights_2 = prompt_weights + [1.0] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
+
+ embeds = []
+ neg_embeds = []
+
+ prompt_token_groups, prompt_weight_groups = group_tokens_and_weights(prompt_tokens.copy(), prompt_weights.copy())
+
+ neg_prompt_token_groups, neg_prompt_weight_groups = group_tokens_and_weights(
+ neg_prompt_tokens.copy(), neg_prompt_weights.copy()
+ )
+
+ prompt_token_groups_2, prompt_weight_groups_2 = group_tokens_and_weights(
+ prompt_tokens_2.copy(), prompt_weights_2.copy()
+ )
+
+ neg_prompt_token_groups_2, neg_prompt_weight_groups_2 = group_tokens_and_weights(
+ neg_prompt_tokens_2.copy(), neg_prompt_weights_2.copy()
+ )
+
+ # get prompt embeddings one by one is not working.
+ for i in range(len(prompt_token_groups)):
+ # get positive prompt embeddings with weights
+ token_tensor = torch.tensor([prompt_token_groups[i]], dtype=torch.long, device=device)
+ weight_tensor = torch.tensor(prompt_weight_groups[i], dtype=torch.float16, device=device)
+
+ token_tensor_2 = torch.tensor([prompt_token_groups_2[i]], dtype=torch.long, device=device)
+
+ # use first text encoder
+ prompt_embeds_1 = pipe.text_encoder(token_tensor.to(device), output_hidden_states=True)
+
+ # use second text encoder
+ prompt_embeds_2 = pipe.text_encoder_2(token_tensor_2.to(device), output_hidden_states=True)
+ pooled_prompt_embeds = prompt_embeds_2[0]
+
+ if clip_skip is None:
+ prompt_embeds_1_hidden_states = prompt_embeds_1.hidden_states[-2]
+ prompt_embeds_2_hidden_states = prompt_embeds_2.hidden_states[-2]
+ else:
+ # "2" because SDXL always indexes from the penultimate layer.
+ prompt_embeds_1_hidden_states = prompt_embeds_1.hidden_states[-(clip_skip + 2)]
+ prompt_embeds_2_hidden_states = prompt_embeds_2.hidden_states[-(clip_skip + 2)]
+
+ prompt_embeds_list = [prompt_embeds_1_hidden_states, prompt_embeds_2_hidden_states]
+ token_embedding = torch.concat(prompt_embeds_list, dim=-1).squeeze(0)
+
+ for j in range(len(weight_tensor)):
+ if weight_tensor[j] != 1.0:
+ token_embedding[j] = (
+ token_embedding[-1] + (token_embedding[j] - token_embedding[-1]) * weight_tensor[j]
+ )
+
+ token_embedding = token_embedding.unsqueeze(0)
+ embeds.append(token_embedding)
+
+ # get negative prompt embeddings with weights
+ neg_token_tensor = torch.tensor([neg_prompt_token_groups[i]], dtype=torch.long, device=device)
+ neg_token_tensor_2 = torch.tensor([neg_prompt_token_groups_2[i]], dtype=torch.long, device=device)
+ neg_weight_tensor = torch.tensor(neg_prompt_weight_groups[i], dtype=torch.float16, device=device)
+
+ # use first text encoder
+ neg_prompt_embeds_1 = pipe.text_encoder(neg_token_tensor.to(device), output_hidden_states=True)
+ neg_prompt_embeds_1_hidden_states = neg_prompt_embeds_1.hidden_states[-2]
+
+ # use second text encoder
+ neg_prompt_embeds_2 = pipe.text_encoder_2(neg_token_tensor_2.to(device), output_hidden_states=True)
+ neg_prompt_embeds_2_hidden_states = neg_prompt_embeds_2.hidden_states[-2]
+ negative_pooled_prompt_embeds = neg_prompt_embeds_2[0]
+
+ neg_prompt_embeds_list = [neg_prompt_embeds_1_hidden_states, neg_prompt_embeds_2_hidden_states]
+ neg_token_embedding = torch.concat(neg_prompt_embeds_list, dim=-1).squeeze(0)
+
+ for z in range(len(neg_weight_tensor)):
+ if neg_weight_tensor[z] != 1.0:
+ neg_token_embedding[z] = (
+ neg_token_embedding[-1] + (neg_token_embedding[z] - neg_token_embedding[-1]) * neg_weight_tensor[z]
+ )
+
+ neg_token_embedding = neg_token_embedding.unsqueeze(0)
+ neg_embeds.append(neg_token_embedding)
+
+ prompt_embeds = torch.cat(embeds, dim=1)
+ negative_prompt_embeds = torch.cat(neg_embeds, dim=1)
+
+ bs_embed, seq_len, _ = prompt_embeds.shape
+ # duplicate text embeddings for each generation per prompt, using mps friendly method
+ prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+ prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+ seq_len = negative_prompt_embeds.shape[1]
+ negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+ negative_prompt_embeds = negative_prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+ pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt, 1).view(
+ bs_embed * num_images_per_prompt, -1
+ )
+ negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt, 1).view(
+ bs_embed * num_images_per_prompt, -1
+ )
+
+ return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
- text_embeddings.append(text_embedding)
- text_embeddings = np.concatenate(text_embeddings, axis=1)
+
+# -------------------------------------------------------------------------------------------------------------------------------
+# reuse the backbone code from StableDiffusionXLPipeline
+# -------------------------------------------------------------------------------------------------------------------------------
+
+logger = logging.get_logger(__name__) # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+ Examples:
+ ```py
+ from diffusers import DiffusionPipeline
+ import torch
+
+ pipe = DiffusionPipeline.from_pretrained(
+ "stabilityai/stable-diffusion-xl-base-1.0"
+ , torch_dtype = torch.float16
+ , use_safetensors = True
+ , variant = "fp16"
+ , custom_pipeline = "lpw_stable_diffusion_xl",
+ )
+
+ prompt = "a white cat running on the grass"*20
+ prompt2 = "play a football"*20
+ prompt = f"{prompt},{prompt2}"
+ neg_prompt = "blur, low quality"
+
+ pipe.to("cuda")
+ images = pipe(
+ prompt = prompt
+ , negative_prompt = neg_prompt
+ ).images[0]
+
+ pipe.to("cpu")
+ torch.cuda.empty_cache()
+ images
+ ```
+"""
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+ """
+ Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+ Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+ """
+ std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+ std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+ # rescale the results from guidance (fixes overexposure)
+ noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+ # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+ noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+ return noise_cfg
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(
+ encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
+):
+ if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+ return encoder_output.latent_dist.sample(generator)
+ elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+ return encoder_output.latent_dist.mode()
+ elif hasattr(encoder_output, "latents"):
+ return encoder_output.latents
else:
- text_embeddings = pipe.text_encoder(input_ids=text_input)[0]
- return text_embeddings
-
-
-def get_weighted_text_embeddings(
- pipe,
- prompt: Union[str, List[str]],
- uncond_prompt: Optional[Union[str, List[str]]] = None,
- max_embeddings_multiples: Optional[int] = 4,
- no_boseos_middle: Optional[bool] = False,
- skip_parsing: Optional[bool] = False,
- skip_weighting: Optional[bool] = False,
+ raise AttributeError("Could not access latents of provided encoder_output")
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+ scheduler,
+ num_inference_steps: Optional[int] = None,
+ device: Optional[Union[str, torch.device]] = None,
+ timesteps: Optional[List[int]] = None,
**kwargs,
+):
+ """
+ Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+ custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+ Args:
+ scheduler (`SchedulerMixin`):
+ The scheduler to get timesteps from.
+ num_inference_steps (`int`):
+ The number of diffusion steps used when generating samples with a pre-trained model. If used,
+ `timesteps` must be `None`.
+ device (`str` or `torch.device`, *optional*):
+ The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+ timesteps (`List[int]`, *optional*):
+ Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
+ timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
+ must be `None`.
+
+ Returns:
+ `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+ second element is the number of inference steps.
+ """
+ if timesteps is not None:
+ accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+ if not accepts_timesteps:
+ raise ValueError(
+ f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+ f" timestep schedules. Please check whether you are using the correct scheduler."
+ )
+ scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+ timesteps = scheduler.timesteps
+ num_inference_steps = len(timesteps)
+ else:
+ scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+ timesteps = scheduler.timesteps
+ return timesteps, num_inference_steps
+
+
+class SDXLLongPromptWeightingPipeline(
+ DiffusionPipeline, FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
):
r"""
- Prompts can be assigned with local weights using brackets. For example,
- prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
- and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.
+ Pipeline for text-to-image generation using Stable Diffusion XL.
- Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
+ This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+ implemented for all pipelines (downloading, saving, running on a particular device, etc.).
+
+ The pipeline also inherits the following loading methods:
+ - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
+ - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
+ - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
+ - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
+ - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
Args:
- pipe (`OnnxStableDiffusionPipeline`):
- Pipe to provide access to the tokenizer and the text encoder.
- prompt (`str` or `List[str]`):
- The prompt or prompts to guide the image generation.
- uncond_prompt (`str` or `List[str]`):
- The unconditional prompt or prompts for guide the image generation. If unconditional prompt
- is provided, the embeddings of prompt and uncond_prompt are concatenated.
- max_embeddings_multiples (`int`, *optional*, defaults to `1`):
- The max multiple length of prompt embeddings compared to the max output length of text encoder.
- no_boseos_middle (`bool`, *optional*, defaults to `False`):
- If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
- ending token in each of the chunk in the middle.
- skip_parsing (`bool`, *optional*, defaults to `False`):
- Skip the parsing of brackets.
- skip_weighting (`bool`, *optional*, defaults to `False`):
- Skip the weighting. When the parsing is skipped, it is forced True.
+ vae ([`AutoencoderKL`]):
+ Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+ text_encoder ([`CLIPTextModel`]):
+ Frozen text-encoder. Stable Diffusion XL uses the text portion of
+ [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+ the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+ text_encoder_2 ([` CLIPTextModelWithProjection`]):
+ Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
+ [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
+ specifically the
+ [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
+ variant.
+ tokenizer (`CLIPTokenizer`):
+ Tokenizer of class
+ [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+ tokenizer_2 (`CLIPTokenizer`):
+ Second Tokenizer of class
+ [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+ unet ([`UNet2DConditionModel`]):
+ Conditional U-Net architecture to denoise the encoded image latents.
+ scheduler ([`SchedulerMixin`]):
+ A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+ [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+ feature_extractor ([`~transformers.CLIPImageProcessor`]):
+ A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
- max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
- if isinstance(prompt, str):
- prompt = [prompt]
-
- if not skip_parsing:
- prompt_tokens, prompt_weights = get_prompts_with_weights(pipe, prompt, max_length - 2)
- if uncond_prompt is not None:
- if isinstance(uncond_prompt, str):
- uncond_prompt = [uncond_prompt]
- uncond_tokens, uncond_weights = get_prompts_with_weights(pipe, uncond_prompt, max_length - 2)
- else:
- prompt_tokens = [
- token[1:-1]
- for token in pipe.tokenizer(prompt, max_length=max_length, truncation=True, return_tensors="np").input_ids
- ]
- prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
- if uncond_prompt is not None:
- if isinstance(uncond_prompt, str):
- uncond_prompt = [uncond_prompt]
- uncond_tokens = [
- token[1:-1]
- for token in pipe.tokenizer(
- uncond_prompt,
+
+ model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae"
+ _optional_components = [
+ "tokenizer",
+ "tokenizer_2",
+ "text_encoder",
+ "text_encoder_2",
+ "image_encoder",
+ "feature_extractor",
+ ]
+ _callback_tensor_inputs = [
+ "latents",
+ "prompt_embeds",
+ "negative_prompt_embeds",
+ "add_text_embeds",
+ "add_time_ids",
+ "negative_pooled_prompt_embeds",
+ "negative_add_time_ids",
+ ]
+
+ def __init__(
+ self,
+ vae: AutoencoderKL,
+ text_encoder: CLIPTextModel,
+ text_encoder_2: CLIPTextModelWithProjection,
+ tokenizer: CLIPTokenizer,
+ tokenizer_2: CLIPTokenizer,
+ unet: UNet2DConditionModel,
+ scheduler: KarrasDiffusionSchedulers,
+ feature_extractor: Optional[CLIPImageProcessor] = None,
+ image_encoder: Optional[CLIPVisionModelWithProjection] = None,
+ force_zeros_for_empty_prompt: bool = True,
+ add_watermarker: Optional[bool] = None,
+ ):
+ super().__init__()
+
+ self.register_modules(
+ vae=vae,
+ text_encoder=text_encoder,
+ text_encoder_2=text_encoder_2,
+ tokenizer=tokenizer,
+ tokenizer_2=tokenizer_2,
+ unet=unet,
+ scheduler=scheduler,
+ feature_extractor=feature_extractor,
+ image_encoder=image_encoder,
+ )
+ self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
+ self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+ self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+ self.mask_processor = VaeImageProcessor(
+ vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True
+ )
+ self.default_sample_size = self.unet.config.sample_size
+
+ add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
+
+ if add_watermarker:
+ self.watermark = StableDiffusionXLWatermarker()
+ else:
+ self.watermark = None
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
+ def enable_vae_slicing(self):
+ r"""
+ Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+ compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+ """
+ self.vae.enable_slicing()
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
+ def disable_vae_slicing(self):
+ r"""
+ Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
+ computing decoding in one step.
+ """
+ self.vae.disable_slicing()
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
+ def enable_vae_tiling(self):
+ r"""
+ Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+ compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+ processing larger images.
+ """
+ self.vae.enable_tiling()
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
+ def disable_vae_tiling(self):
+ r"""
+ Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
+ computing decoding in one step.
+ """
+ self.vae.disable_tiling()
+
+ def enable_model_cpu_offload(self, gpu_id=0):
+ r"""
+ Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
+ to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
+ method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
+ `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
+ """
+ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
+ from accelerate import cpu_offload_with_hook
+ else:
+ raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
+
+ device = torch.device(f"cuda:{gpu_id}")
+
+ if self.device.type != "cpu":
+ self.to("cpu", silence_dtype_warnings=True)
+ torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
+
+ model_sequence = (
+ [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
+ )
+ model_sequence.extend([self.unet, self.vae])
+
+ hook = None
+ for cpu_offloaded_model in model_sequence:
+ _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
+
+ # We'll offload the last model manually.
+ self.final_offload_hook = hook
+
+ # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
+ def encode_prompt(
+ self,
+ prompt: str,
+ prompt_2: Optional[str] = None,
+ device: Optional[torch.device] = None,
+ num_images_per_prompt: int = 1,
+ do_classifier_free_guidance: bool = True,
+ negative_prompt: Optional[str] = None,
+ negative_prompt_2: Optional[str] = None,
+ prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+ pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ lora_scale: Optional[float] = None,
+ ):
+ r"""
+ Encodes the prompt into text encoder hidden states.
+
+ Args:
+ prompt (`str` or `List[str]`, *optional*):
+ prompt to be encoded
+ prompt_2 (`str` or `List[str]`, *optional*):
+ The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+ used in both text-encoders
+ device: (`torch.device`):
+ torch device
+ num_images_per_prompt (`int`):
+ number of images that should be generated per prompt
+ do_classifier_free_guidance (`bool`):
+ whether to use classifier free guidance or not
+ negative_prompt (`str` or `List[str]`, *optional*):
+ The prompt or prompts not to guide the image generation. If not defined, one has to pass
+ `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+ less than `1`).
+ negative_prompt_2 (`str` or `List[str]`, *optional*):
+ The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+ `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+ prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+ provided, text embeddings will be generated from `prompt` input argument.
+ negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+ weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+ argument.
+ pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+ If not provided, pooled text embeddings will be generated from `prompt` input argument.
+ negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+ weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+ input argument.
+ lora_scale (`float`, *optional*):
+ A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+ """
+ device = device or self._execution_device
+
+ # set lora scale so that monkey patched LoRA
+ # function of text encoder can correctly access it
+ if lora_scale is not None and isinstance(self, LoraLoaderMixin):
+ self._lora_scale = lora_scale
+
+ if prompt is not None and isinstance(prompt, str):
+ batch_size = 1
+ elif prompt is not None and isinstance(prompt, list):
+ batch_size = len(prompt)
+ else:
+ batch_size = prompt_embeds.shape[0]
+
+ # Define tokenizers and text encoders
+ tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
+ text_encoders = (
+ [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
+ )
+
+ if prompt_embeds is None:
+ prompt_2 = prompt_2 or prompt
+ # textual inversion: procecss multi-vector tokens if necessary
+ prompt_embeds_list = []
+ prompts = [prompt, prompt_2]
+ for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
+ if isinstance(self, TextualInversionLoaderMixin):
+ prompt = self.maybe_convert_prompt(prompt, tokenizer)
+
+ text_inputs = tokenizer(
+ prompt,
+ padding="max_length",
+ max_length=tokenizer.model_max_length,
+ truncation=True,
+ return_tensors="pt",
+ )
+
+ text_input_ids = text_inputs.input_ids
+ untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+ if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+ text_input_ids, untruncated_ids
+ ):
+ removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
+ logger.warning(
+ "The following part of your input was truncated because CLIP can only handle sequences up to"
+ f" {tokenizer.model_max_length} tokens: {removed_text}"
+ )
+
+ prompt_embeds = text_encoder(
+ text_input_ids.to(device),
+ output_hidden_states=True,
+ )
+
+ # We are only ALWAYS interested in the pooled output of the final text encoder
+ pooled_prompt_embeds = prompt_embeds[0]
+ prompt_embeds = prompt_embeds.hidden_states[-2]
+
+ prompt_embeds_list.append(prompt_embeds)
+
+ prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+ # get unconditional embeddings for classifier free guidance
+ zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
+ if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
+ negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+ negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
+ elif do_classifier_free_guidance and negative_prompt_embeds is None:
+ negative_prompt = negative_prompt or ""
+ negative_prompt_2 = negative_prompt_2 or negative_prompt
+
+ uncond_tokens: List[str]
+ if prompt is not None and type(prompt) is not type(negative_prompt):
+ raise TypeError(
+ f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+ f" {type(prompt)}."
+ )
+ elif isinstance(negative_prompt, str):
+ uncond_tokens = [negative_prompt, negative_prompt_2]
+ elif batch_size != len(negative_prompt):
+ raise ValueError(
+ f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+ f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+ " the batch size of `prompt`."
+ )
+ else:
+ uncond_tokens = [negative_prompt, negative_prompt_2]
+
+ negative_prompt_embeds_list = []
+ for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
+ if isinstance(self, TextualInversionLoaderMixin):
+ negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
+
+ max_length = prompt_embeds.shape[1]
+ uncond_input = tokenizer(
+ negative_prompt,
+ padding="max_length",
max_length=max_length,
truncation=True,
- return_tensors="np",
- ).input_ids
- ]
- uncond_weights = [[1.0] * len(token) for token in uncond_tokens]
+ return_tensors="pt",
+ )
- # round up the longest length of tokens to a multiple of (model_max_length - 2)
- max_length = max([len(token) for token in prompt_tokens])
- if uncond_prompt is not None:
- max_length = max(max_length, max([len(token) for token in uncond_tokens]))
+ negative_prompt_embeds = text_encoder(
+ uncond_input.input_ids.to(device),
+ output_hidden_states=True,
+ )
+ # We are only ALWAYS interested in the pooled output of the final text encoder
+ negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+ negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
- max_embeddings_multiples = min(
- max_embeddings_multiples,
- (max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,
- )
- max_embeddings_multiples = max(1, max_embeddings_multiples)
- max_length = (pipe.tokenizer.model_max_length - 2) * max_embeddings_multiples + 2
+ negative_prompt_embeds_list.append(negative_prompt_embeds)
- # pad the length of tokens and weights
- bos = pipe.tokenizer.bos_token_id
- eos = pipe.tokenizer.eos_token_id
- pad = getattr(pipe.tokenizer, "pad_token_id", eos)
- prompt_tokens, prompt_weights = pad_tokens_and_weights(
- prompt_tokens,
- prompt_weights,
- max_length,
- bos,
- eos,
- pad,
- no_boseos_middle=no_boseos_middle,
- chunk_length=pipe.tokenizer.model_max_length,
- )
- prompt_tokens = np.array(prompt_tokens, dtype=np.int32)
- if uncond_prompt is not None:
- uncond_tokens, uncond_weights = pad_tokens_and_weights(
- uncond_tokens,
- uncond_weights,
- max_length,
- bos,
- eos,
- pad,
- no_boseos_middle=no_boseos_middle,
- chunk_length=pipe.tokenizer.model_max_length,
- )
- uncond_tokens = np.array(uncond_tokens, dtype=np.int32)
-
- # get the embeddings
- text_embeddings = get_unweighted_text_embeddings(
- pipe,
- prompt_tokens,
- pipe.tokenizer.model_max_length,
- no_boseos_middle=no_boseos_middle,
- )
- prompt_weights = np.array(prompt_weights, dtype=text_embeddings.dtype)
- if uncond_prompt is not None:
- uncond_embeddings = get_unweighted_text_embeddings(
- pipe,
- uncond_tokens,
- pipe.tokenizer.model_max_length,
- no_boseos_middle=no_boseos_middle,
+ negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+
+ prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+ bs_embed, seq_len, _ = prompt_embeds.shape
+ # duplicate text embeddings for each generation per prompt, using mps friendly method
+ prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+ prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+ if do_classifier_free_guidance:
+ # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+ seq_len = negative_prompt_embeds.shape[1]
+ negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+ negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+ negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+ pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+ bs_embed * num_images_per_prompt, -1
)
- uncond_weights = np.array(uncond_weights, dtype=uncond_embeddings.dtype)
-
- # assign weights to the prompts and normalize in the sense of mean
- # TODO: should we normalize by chunk or in a whole (current implementation)?
- if (not skip_parsing) and (not skip_weighting):
- previous_mean = text_embeddings.mean(axis=(-2, -1))
- text_embeddings *= prompt_weights[:, :, None]
- text_embeddings *= (previous_mean / text_embeddings.mean(axis=(-2, -1)))[:, None, None]
- if uncond_prompt is not None:
- previous_mean = uncond_embeddings.mean(axis=(-2, -1))
- uncond_embeddings *= uncond_weights[:, :, None]
- uncond_embeddings *= (previous_mean / uncond_embeddings.mean(axis=(-2, -1)))[:, None, None]
-
- # For classifier free guidance, we need to do two forward passes.
- # Here we concatenate the unconditional and text embeddings into a single batch
- # to avoid doing two forward passes
- if uncond_prompt is not None:
- return text_embeddings, uncond_embeddings
-
- return text_embeddings
-
-
-def preprocess_image(image):
- w, h = image.size
- w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
- image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
- image = np.array(image).astype(np.float32) / 255.0
- image = image[None].transpose(0, 3, 1, 2)
- return 2.0 * image - 1.0
-
-
-def preprocess_mask(mask, scale_factor=8):
- mask = mask.convert("L")
- w, h = mask.size
- w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
- mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"])
- mask = np.array(mask).astype(np.float32) / 255.0
- mask = np.tile(mask, (4, 1, 1))
- mask = mask[None].transpose(0, 1, 2, 3) # what does this step do?
- mask = 1 - mask # repaint white, keep black
- return mask
-
-
-class OnnxStableDiffusionLongPromptWeightingPipeline(OnnxStableDiffusionPipeline):
- r"""
- Pipeline for text-to-image generation using Stable Diffusion without tokens length limit, and support parsing
- weighting in prompt.
+ if do_classifier_free_guidance:
+ negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+ bs_embed * num_images_per_prompt, -1
+ )
- This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
- library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
- """
+ return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
- if version.parse(version.parse(diffusers.__version__).base_version) >= version.parse("0.9.0"):
-
- def __init__(
- self,
- vae_encoder: OnnxRuntimeModel,
- vae_decoder: OnnxRuntimeModel,
- text_encoder: OnnxRuntimeModel,
- tokenizer: CLIPTokenizer,
- unet: OnnxRuntimeModel,
- scheduler: SchedulerMixin,
- safety_checker: OnnxRuntimeModel,
- feature_extractor: CLIPImageProcessor,
- requires_safety_checker: bool = True,
- ):
- super().__init__(
- vae_encoder=vae_encoder,
- vae_decoder=vae_decoder,
- text_encoder=text_encoder,
- tokenizer=tokenizer,
- unet=unet,
- scheduler=scheduler,
- safety_checker=safety_checker,
- feature_extractor=feature_extractor,
- requires_safety_checker=requires_safety_checker,
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
+ def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
+ dtype = next(self.image_encoder.parameters()).dtype
+
+ if not isinstance(image, torch.Tensor):
+ image = self.feature_extractor(image, return_tensors="pt").pixel_values
+
+ image = image.to(device=device, dtype=dtype)
+ if output_hidden_states:
+ image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
+ image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
+ uncond_image_enc_hidden_states = self.image_encoder(
+ torch.zeros_like(image), output_hidden_states=True
+ ).hidden_states[-2]
+ uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
+ num_images_per_prompt, dim=0
)
- self.__init__additional__()
+ return image_enc_hidden_states, uncond_image_enc_hidden_states
+ else:
+ image_embeds = self.image_encoder(image).image_embeds
+ image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
+ uncond_image_embeds = torch.zeros_like(image_embeds)
- else:
+ return image_embeds, uncond_image_embeds
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+ def prepare_extra_step_kwargs(self, generator, eta):
+ # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+ # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+ # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+ # and should be between [0, 1]
+
+ accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+ extra_step_kwargs = {}
+ if accepts_eta:
+ extra_step_kwargs["eta"] = eta
+
+ # check if the scheduler accepts generator
+ accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+ if accepts_generator:
+ extra_step_kwargs["generator"] = generator
+ return extra_step_kwargs
+
+ def check_inputs(
+ self,
+ prompt,
+ prompt_2,
+ height,
+ width,
+ strength,
+ callback_steps,
+ negative_prompt=None,
+ negative_prompt_2=None,
+ prompt_embeds=None,
+ negative_prompt_embeds=None,
+ pooled_prompt_embeds=None,
+ negative_pooled_prompt_embeds=None,
+ callback_on_step_end_tensor_inputs=None,
+ ):
+ if height % 8 != 0 or width % 8 != 0:
+ raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
- def __init__(
- self,
- vae_encoder: OnnxRuntimeModel,
- vae_decoder: OnnxRuntimeModel,
- text_encoder: OnnxRuntimeModel,
- tokenizer: CLIPTokenizer,
- unet: OnnxRuntimeModel,
- scheduler: SchedulerMixin,
- safety_checker: OnnxRuntimeModel,
- feature_extractor: CLIPImageProcessor,
+ if strength < 0 or strength > 1:
+ raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
+
+ if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
+ raise ValueError(
+ f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+ f" {type(callback_steps)}."
+ )
+
+ if callback_on_step_end_tensor_inputs is not None and not all(
+ k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
- super().__init__(
- vae_encoder=vae_encoder,
- vae_decoder=vae_decoder,
- text_encoder=text_encoder,
- tokenizer=tokenizer,
- unet=unet,
- scheduler=scheduler,
- safety_checker=safety_checker,
- feature_extractor=feature_extractor,
+ raise ValueError(
+ f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+ )
+
+ if prompt is not None and prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+ " only forward one of the two."
+ )
+ elif prompt_2 is not None and prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+ " only forward one of the two."
+ )
+ elif prompt is None and prompt_embeds is None:
+ raise ValueError(
+ "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+ )
+ elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+ raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+ elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
+ raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
+
+ if negative_prompt is not None and negative_prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+ f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+ )
+ elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
+ f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+ )
+
+ if prompt_embeds is not None and negative_prompt_embeds is not None:
+ if prompt_embeds.shape != negative_prompt_embeds.shape:
+ raise ValueError(
+ "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+ f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+ f" {negative_prompt_embeds.shape}."
+ )
+
+ if prompt_embeds is not None and pooled_prompt_embeds is None:
+ raise ValueError(
+ "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
+ )
+
+ if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
+ raise ValueError(
+ "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
+ )
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu
+ def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
+ r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.
+
+ The suffixes after the scaling factors represent the stages where they are being applied.
+
+ Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values
+ that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
+
+ Args:
+ s1 (`float`):
+ Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
+ mitigate "oversmoothing effect" in the enhanced denoising process.
+ s2 (`float`):
+ Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
+ mitigate "oversmoothing effect" in the enhanced denoising process.
+ b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
+ b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
+ """
+ if not hasattr(self, "unet"):
+ raise ValueError("The pipeline must have `unet` for using FreeU.")
+ self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu
+ def disable_freeu(self):
+ """Disables the FreeU mechanism if enabled."""
+ self.unet.disable_freeu()
+
+ # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.fuse_qkv_projections
+ def fuse_qkv_projections(self, unet: bool = True, vae: bool = True):
+ """
+ Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query,
+ key, value) are fused. For cross-attention modules, key and value projection matrices are fused.
+
+
+
+ This API is 🧪 experimental.
+
+
+
+ Args:
+ unet (`bool`, defaults to `True`): To apply fusion on the UNet.
+ vae (`bool`, defaults to `True`): To apply fusion on the VAE.
+ """
+ self.fusing_unet = False
+ self.fusing_vae = False
+
+ if unet:
+ self.fusing_unet = True
+ self.unet.fuse_qkv_projections()
+ self.unet.set_attn_processor(FusedAttnProcessor2_0())
+
+ if vae:
+ if not isinstance(self.vae, AutoencoderKL):
+ raise ValueError("`fuse_qkv_projections()` is only supported for the VAE of type `AutoencoderKL`.")
+
+ self.fusing_vae = True
+ self.vae.fuse_qkv_projections()
+ self.vae.set_attn_processor(FusedAttnProcessor2_0())
+
+ # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.unfuse_qkv_projections
+ def unfuse_qkv_projections(self, unet: bool = True, vae: bool = True):
+ """Disable QKV projection fusion if enabled.
+
+
+
+ This API is 🧪 experimental.
+
+
+
+ Args:
+ unet (`bool`, defaults to `True`): To apply fusion on the UNet.
+ vae (`bool`, defaults to `True`): To apply fusion on the VAE.
+
+ """
+ if unet:
+ if not self.fusing_unet:
+ logger.warning("The UNet was not initially fused for QKV projections. Doing nothing.")
+ else:
+ self.unet.unfuse_qkv_projections()
+ self.fusing_unet = False
+
+ if vae:
+ if not self.fusing_vae:
+ logger.warning("The VAE was not initially fused for QKV projections. Doing nothing.")
+ else:
+ self.vae.unfuse_qkv_projections()
+ self.fusing_vae = False
+
+ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None):
+ # get the original timestep using init_timestep
+ if denoising_start is None:
+ init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+ t_start = max(num_inference_steps - init_timestep, 0)
+ else:
+ t_start = 0
+
+ timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+
+ # Strength is irrelevant if we directly request a timestep to start at;
+ # that is, strength is determined by the denoising_start instead.
+ if denoising_start is not None:
+ discrete_timestep_cutoff = int(
+ round(
+ self.scheduler.config.num_train_timesteps
+ - (denoising_start * self.scheduler.config.num_train_timesteps)
+ )
)
- self.__init__additional__()
- def __init__additional__(self):
- self.unet.config.in_channels = 4
- self.vae_scale_factor = 8
+ num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()
+ if self.scheduler.order == 2 and num_inference_steps % 2 == 0:
+ # if the scheduler is a 2nd order scheduler we might have to do +1
+ # because `num_inference_steps` might be even given that every timestep
+ # (except the highest one) is duplicated. If `num_inference_steps` is even it would
+ # mean that we cut the timesteps in the middle of the denoising step
+ # (between 1st and 2nd devirative) which leads to incorrect results. By adding 1
+ # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler
+ num_inference_steps = num_inference_steps + 1
+
+ # because t_n+1 >= t_n, we slice the timesteps starting from the end
+ timesteps = timesteps[-num_inference_steps:]
+ return timesteps, num_inference_steps
+
+ return timesteps, num_inference_steps - t_start
+
+ def prepare_latents(
+ self,
+ image,
+ mask,
+ width,
+ height,
+ num_channels_latents,
+ timestep,
+ batch_size,
+ num_images_per_prompt,
+ dtype,
+ device,
+ generator=None,
+ add_noise=True,
+ latents=None,
+ is_strength_max=True,
+ return_noise=False,
+ return_image_latents=False,
+ ):
+ batch_size *= num_images_per_prompt
+
+ if image is None:
+ shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+ if isinstance(generator, list) and len(generator) != batch_size:
+ raise ValueError(
+ f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+ f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+ )
+
+ if latents is None:
+ latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+ else:
+ latents = latents.to(device)
+
+ # scale the initial noise by the standard deviation required by the scheduler
+ latents = latents * self.scheduler.init_noise_sigma
+ return latents
+
+ elif mask is None:
+ if not isinstance(image, (torch.Tensor, Image.Image, list)):
+ raise ValueError(
+ f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
+ )
+
+ # Offload text encoder if `enable_model_cpu_offload` was enabled
+ if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+ self.text_encoder_2.to("cpu")
+ torch.cuda.empty_cache()
+
+ image = image.to(device=device, dtype=dtype)
+
+ if image.shape[1] == 4:
+ init_latents = image
+
+ else:
+ # make sure the VAE is in float32 mode, as it overflows in float16
+ if self.vae.config.force_upcast:
+ image = image.float()
+ self.vae.to(dtype=torch.float32)
+
+ if isinstance(generator, list) and len(generator) != batch_size:
+ raise ValueError(
+ f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+ f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+ )
+
+ elif isinstance(generator, list):
+ init_latents = [
+ retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
+ for i in range(batch_size)
+ ]
+ init_latents = torch.cat(init_latents, dim=0)
+ else:
+ init_latents = retrieve_latents(self.vae.encode(image), generator=generator)
+
+ if self.vae.config.force_upcast:
+ self.vae.to(dtype)
+
+ init_latents = init_latents.to(dtype)
+ init_latents = self.vae.config.scaling_factor * init_latents
+
+ if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
+ # expand init_latents for batch_size
+ additional_image_per_prompt = batch_size // init_latents.shape[0]
+ init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
+ elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
+ raise ValueError(
+ f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
+ )
+ else:
+ init_latents = torch.cat([init_latents], dim=0)
+
+ if add_noise:
+ shape = init_latents.shape
+ noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+ # get latents
+ init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+
+ latents = init_latents
+ return latents
+
+ else:
+ shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+ if isinstance(generator, list) and len(generator) != batch_size:
+ raise ValueError(
+ f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+ f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+ )
+
+ if (image is None or timestep is None) and not is_strength_max:
+ raise ValueError(
+ "Since strength < 1. initial latents are to be initialised as a combination of Image + Noise."
+ "However, either the image or the noise timestep has not been provided."
+ )
+
+ if image.shape[1] == 4:
+ image_latents = image.to(device=device, dtype=dtype)
+ image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)
+ elif return_image_latents or (latents is None and not is_strength_max):
+ image = image.to(device=device, dtype=dtype)
+ image_latents = self._encode_vae_image(image=image, generator=generator)
+ image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)
+
+ if latents is None and add_noise:
+ noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+ # if strength is 1. then initialise the latents to noise, else initial to image + noise
+ latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
+ # if pure noise then scale the initial latents by the Scheduler's init sigma
+ latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents
+ elif add_noise:
+ noise = latents.to(device)
+ latents = noise * self.scheduler.init_noise_sigma
+ else:
+ noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+ latents = image_latents.to(device)
- def encode_prompt(
- self,
- prompt,
- num_images_per_prompt,
- do_classifier_free_guidance,
- negative_prompt,
- max_embeddings_multiples,
- ):
- r"""
- Encodes the prompt into text encoder hidden states.
+ outputs = (latents,)
- Args:
- prompt (`str` or `list(int)`):
- prompt to be encoded
- num_images_per_prompt (`int`):
- number of images that should be generated per prompt
- do_classifier_free_guidance (`bool`):
- whether to use classifier free guidance or not
- negative_prompt (`str` or `List[str]`):
- The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
- if `guidance_scale` is less than `1`).
- max_embeddings_multiples (`int`, *optional*, defaults to `3`):
- The max multiple length of prompt embeddings compared to the max output length of text encoder.
- """
- batch_size = len(prompt) if isinstance(prompt, list) else 1
+ if return_noise:
+ outputs += (noise,)
- if negative_prompt is None:
- negative_prompt = [""] * batch_size
- elif isinstance(negative_prompt, str):
- negative_prompt = [negative_prompt] * batch_size
- if batch_size != len(negative_prompt):
- raise ValueError(
- f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
- f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
- " the batch size of `prompt`."
- )
+ if return_image_latents:
+ outputs += (image_latents,)
- text_embeddings, uncond_embeddings = get_weighted_text_embeddings(
- pipe=self,
- prompt=prompt,
- uncond_prompt=negative_prompt if do_classifier_free_guidance else None,
- max_embeddings_multiples=max_embeddings_multiples,
- )
+ return outputs
- text_embeddings = text_embeddings.repeat(num_images_per_prompt, 0)
- if do_classifier_free_guidance:
- uncond_embeddings = uncond_embeddings.repeat(num_images_per_prompt, 0)
- text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
+ def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
+ dtype = image.dtype
+ if self.vae.config.force_upcast:
+ image = image.float()
+ self.vae.to(dtype=torch.float32)
- return text_embeddings
+ if isinstance(generator, list):
+ image_latents = [
+ retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
+ for i in range(image.shape[0])
+ ]
+ image_latents = torch.cat(image_latents, dim=0)
+ else:
+ image_latents = retrieve_latents(self.vae.encode(image), generator=generator)
- def check_inputs(self, prompt, height, width, strength, callback_steps):
- if not isinstance(prompt, str) and not isinstance(prompt, list):
- raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+ if self.vae.config.force_upcast:
+ self.vae.to(dtype)
- if strength < 0 or strength > 1:
- raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
+ image_latents = image_latents.to(dtype)
+ image_latents = self.vae.config.scaling_factor * image_latents
- if height % 8 != 0 or width % 8 != 0:
- raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+ return image_latents
- if (callback_steps is None) or (
- callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
- ):
- raise ValueError(
- f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
- f" {type(callback_steps)}."
- )
+ def prepare_mask_latents(
+ self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
+ ):
+ # resize the mask to latents shape as we concatenate the mask to the latents
+ # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
+ # and half precision
+ mask = torch.nn.functional.interpolate(
+ mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
+ )
+ mask = mask.to(device=device, dtype=dtype)
+
+ # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
+ if mask.shape[0] < batch_size:
+ if not batch_size % mask.shape[0] == 0:
+ raise ValueError(
+ "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
+ f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
+ " of masks that you pass is divisible by the total requested batch size."
+ )
+ mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
- def get_timesteps(self, num_inference_steps, strength, is_text2img):
- if is_text2img:
- return self.scheduler.timesteps, num_inference_steps
+ mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
+
+ if masked_image is not None and masked_image.shape[1] == 4:
+ masked_image_latents = masked_image
else:
- # get the original timestep using init_timestep
- offset = self.scheduler.config.get("steps_offset", 0)
- init_timestep = int(num_inference_steps * strength) + offset
- init_timestep = min(init_timestep, num_inference_steps)
-
- t_start = max(num_inference_steps - init_timestep + offset, 0)
- timesteps = self.scheduler.timesteps[t_start:]
- return timesteps, num_inference_steps - t_start
-
- def run_safety_checker(self, image):
- if self.safety_checker is not None:
- safety_checker_input = self.feature_extractor(
- self.numpy_to_pil(image), return_tensors="np"
- ).pixel_values.astype(image.dtype)
- # There will throw an error if use safety_checker directly and batchsize>1
- images, has_nsfw_concept = [], []
- for i in range(image.shape[0]):
- image_i, has_nsfw_concept_i = self.safety_checker(
- clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
+ masked_image_latents = None
+
+ if masked_image is not None:
+ if masked_image_latents is None:
+ masked_image = masked_image.to(device=device, dtype=dtype)
+ masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
+
+ if masked_image_latents.shape[0] < batch_size:
+ if not batch_size % masked_image_latents.shape[0] == 0:
+ raise ValueError(
+ "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+ f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
+ " Make sure the number of images that you pass is divisible by the total requested batch size."
+ )
+ masked_image_latents = masked_image_latents.repeat(
+ batch_size // masked_image_latents.shape[0], 1, 1, 1
)
- images.append(image_i)
- has_nsfw_concept.append(has_nsfw_concept_i[0])
- image = np.concatenate(images)
- else:
- has_nsfw_concept = None
- return image, has_nsfw_concept
-
- def decode_latents(self, latents):
- latents = 1 / 0.18215 * latents
- # image = self.vae_decoder(latent_sample=latents)[0]
- # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
- image = np.concatenate(
- [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
- )
- image = np.clip(image / 2 + 0.5, 0, 1)
- image = image.transpose((0, 2, 3, 1))
- return image
- def prepare_extra_step_kwargs(self, generator, eta):
- # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
- # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
- # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
- # and should be between [0, 1]
+ masked_image_latents = (
+ torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
+ )
- accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
- extra_step_kwargs = {}
- if accepts_eta:
- extra_step_kwargs["eta"] = eta
+ # aligning device to prevent device errors when concating it with the latent model input
+ masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
- # check if the scheduler accepts generator
- accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
- if accepts_generator:
- extra_step_kwargs["generator"] = generator
- return extra_step_kwargs
+ return mask, masked_image_latents
- def prepare_latents(self, image, timestep, batch_size, height, width, dtype, generator, latents=None):
- if image is None:
- shape = (
- batch_size,
- self.unet.config.in_channels,
- height // self.vae_scale_factor,
- width // self.vae_scale_factor,
+ def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, dtype):
+ add_time_ids = list(original_size + crops_coords_top_left + target_size)
+
+ passed_add_embed_dim = (
+ self.unet.config.addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2.config.projection_dim
+ )
+ expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
+
+ if expected_add_embed_dim != passed_add_embed_dim:
+ raise ValueError(
+ f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
)
- if latents is None:
- latents = torch.randn(shape, generator=generator, device="cpu").numpy().astype(dtype)
- else:
- if latents.shape != shape:
- raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+ add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
+ return add_time_ids
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
+ def upcast_vae(self):
+ dtype = self.vae.dtype
+ self.vae.to(dtype=torch.float32)
+ use_torch_2_0_or_xformers = isinstance(
+ self.vae.decoder.mid_block.attentions[0].processor,
+ (
+ AttnProcessor2_0,
+ XFormersAttnProcessor,
+ LoRAXFormersAttnProcessor,
+ LoRAAttnProcessor2_0,
+ ),
+ )
+ # if xformers or torch_2_0 is used attention block does not need
+ # to be in float32 which can save lots of memory
+ if use_torch_2_0_or_xformers:
+ self.vae.post_quant_conv.to(dtype)
+ self.vae.decoder.conv_in.to(dtype)
+ self.vae.decoder.mid_block.to(dtype)
+
+ # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
+ def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+ """
+ See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
- # scale the initial noise by the standard deviation required by the scheduler
- latents = (torch.from_numpy(latents) * self.scheduler.init_noise_sigma).numpy()
- return latents, None, None
- else:
- init_latents = self.vae_encoder(sample=image)[0]
- init_latents = 0.18215 * init_latents
- init_latents = np.concatenate([init_latents] * batch_size, axis=0)
- init_latents_orig = init_latents
- shape = init_latents.shape
-
- # add noise to latents using the timesteps
- noise = torch.randn(shape, generator=generator, device="cpu").numpy().astype(dtype)
- latents = self.scheduler.add_noise(
- torch.from_numpy(init_latents), torch.from_numpy(noise), timestep
- ).numpy()
- return latents, init_latents_orig, noise
+ Args:
+ timesteps (`torch.Tensor`):
+ generate embedding vectors at these timesteps
+ embedding_dim (`int`, *optional*, defaults to 512):
+ dimension of the embeddings to generate
+ dtype:
+ data type of the generated embeddings
+
+ Returns:
+ `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+ """
+ assert len(w.shape) == 1
+ w = w * 1000.0
+
+ half_dim = embedding_dim // 2
+ emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+ emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
+ emb = w.to(dtype)[:, None] * emb[None, :]
+ emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+ if embedding_dim % 2 == 1: # zero pad
+ emb = torch.nn.functional.pad(emb, (0, 1))
+ assert emb.shape == (w.shape[0], embedding_dim)
+ return emb
+
+ @property
+ def guidance_scale(self):
+ return self._guidance_scale
+
+ @property
+ def guidance_rescale(self):
+ return self._guidance_rescale
+
+ @property
+ def clip_skip(self):
+ return self._clip_skip
+
+ # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+ # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+ # corresponds to doing no classifier free guidance.
+ @property
+ def do_classifier_free_guidance(self):
+ return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
+
+ @property
+ def cross_attention_kwargs(self):
+ return self._cross_attention_kwargs
+
+ @property
+ def denoising_end(self):
+ return self._denoising_end
+
+ @property
+ def denoising_start(self):
+ return self._denoising_start
+
+ @property
+ def num_timesteps(self):
+ return self._num_timesteps
@torch.no_grad()
+ @replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
- prompt: Union[str, List[str]],
- negative_prompt: Optional[Union[str, List[str]]] = None,
- image: Union[np.ndarray, PIL.Image.Image] = None,
- mask_image: Union[np.ndarray, PIL.Image.Image] = None,
- height: int = 512,
- width: int = 512,
- num_inference_steps: int = 50,
- guidance_scale: float = 7.5,
+ prompt: str = None,
+ prompt_2: Optional[str] = None,
+ image: Optional[PipelineImageInput] = None,
+ mask_image: Optional[PipelineImageInput] = None,
+ masked_image_latents: Optional[torch.FloatTensor] = None,
+ height: Optional[int] = None,
+ width: Optional[int] = None,
strength: float = 0.8,
+ num_inference_steps: int = 50,
+ timesteps: List[int] = None,
+ denoising_start: Optional[float] = None,
+ denoising_end: Optional[float] = None,
+ guidance_scale: float = 5.0,
+ negative_prompt: Optional[str] = None,
+ negative_prompt_2: Optional[str] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
- generator: Optional[torch.Generator] = None,
- latents: Optional[np.ndarray] = None,
- max_embeddings_multiples: Optional[int] = 3,
+ generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+ latents: Optional[torch.FloatTensor] = None,
+ ip_adapter_image: Optional[PipelineImageInput] = None,
+ prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+ pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
- callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
- is_cancelled_callback: Optional[Callable[[], bool]] = None,
- callback_steps: int = 1,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ guidance_rescale: float = 0.0,
+ original_size: Optional[Tuple[int, int]] = None,
+ crops_coords_top_left: Tuple[int, int] = (0, 0),
+ target_size: Optional[Tuple[int, int]] = None,
+ clip_skip: Optional[int] = None,
+ callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+ callback_on_step_end_tensor_inputs: List[str] = ["latents"],
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
- prompt (`str` or `List[str]`):
- The prompt or prompts to guide the image generation.
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
- if `guidance_scale` is less than `1`).
- image (`np.ndarray` or `PIL.Image.Image`):
+ prompt (`str`):
+ The prompt to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+ instead.
+ prompt_2 (`str`):
+ The prompt to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+ used in both text-encoders
+ image (`PipelineImageInput`, *optional*):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
- mask_image (`np.ndarray` or `PIL.Image.Image`):
+ mask_image (`PipelineImageInput`, *optional*):
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
- height (`int`, *optional*, defaults to 512):
+ height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image.
- width (`int`, *optional*, defaults to 512):
+ width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image.
+ strength (`float`, *optional*, defaults to 0.8):
+ Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
+ `image` will be used as a starting point, adding more noise to it the larger the `strength`. The
+ number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
+ noise will be maximum and the denoising process will run for the full number of iterations specified in
+ `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
- guidance_scale (`float`, *optional*, defaults to 7.5):
+ timesteps (`List[int]`, *optional*):
+ Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+ in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+ passed will be used. Must be in descending order.
+ denoising_start (`float`, *optional*):
+ When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be
+ bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and
+ it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,
+ strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline
+ is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refine Image
+ Quality**](https://huggingface.co/docs/diffusers/using-diffusers/sdxl#refine-image-quality).
+ denoising_end (`float`, *optional*):
+ When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
+ completed before it is intentionally prematurely terminated. As a result, the returned sample will
+ still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be
+ denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the
+ final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline
+ forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refine Image
+ Quality**](https://huggingface.co/docs/diffusers/using-diffusers/sdxl#refine-image-quality).
+ guidance_scale (`float`, *optional*, defaults to 5.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
- strength (`float`, *optional*, defaults to 0.8):
- Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
- `image` will be used as a starting point, adding more noise to it the larger the `strength`. The
- number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
- noise will be maximum and the denoising process will run for the full number of iterations specified in
- `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
+ negative_prompt (`str`):
+ The prompt not to guide the image generation. If not defined, one has to pass
+ `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+ less than `1`).
+ negative_prompt_2 (`str`):
+ The prompt not to guide the image generation to be sent to `tokenizer_2` and
+ `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
- generator (`torch.Generator`, *optional*):
- A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
- deterministic.
- latents (`np.ndarray`, *optional*):
+ generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+ One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+ to make generation deterministic.
+ latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
- max_embeddings_multiples (`int`, *optional*, defaults to `3`):
- The max multiple length of prompt embeddings compared to the max output length of text encoder.
+ ip_adapter_image: (`PipelineImageInput`, *optional*):
+ Optional image input to work with IP Adapters.
+ prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+ provided, text embeddings will be generated from `prompt` input argument.
+ negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+ weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+ argument.
+ pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+ If not provided, pooled text embeddings will be generated from `prompt` input argument.
+ negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+ weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+ input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
- plain tuple.
- callback (`Callable`, *optional*):
- A function that will be called every `callback_steps` steps during inference. The function will be
- called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
- is_cancelled_callback (`Callable`, *optional*):
- A function that will be called every `callback_steps` steps during inference. If the function returns
- `True`, the inference will be cancelled.
- callback_steps (`int`, *optional*, defaults to 1):
- The frequency at which the `callback` function will be called. If not specified, the callback will be
- called at every step.
+ Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+ of a plain tuple.
+ cross_attention_kwargs (`dict`, *optional*):
+ A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+ `self.processor` in
+ [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+ guidance_rescale (`float`, *optional*, defaults to 0.0):
+ Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
+ Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
+ [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+ Guidance rescale factor should fix overexposure when using zero terminal SNR.
+ original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+ If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+ `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
+ explained in section 2.2 of
+ [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+ crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+ `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+ `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+ `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+ [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+ target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+ For most cases, `target_size` should be set to the desired height and width of the generated image. If
+ not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
+ section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+ clip_skip (`int`, *optional*):
+ Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+ the output of the pre-final layer will be used for computing the prompt embeddings.
+ callback_on_step_end (`Callable`, *optional*):
+ A function that calls at the end of each denoising steps during the inference. The function is called
+ with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+ callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+ `callback_on_step_end_tensor_inputs`.
+ callback_on_step_end_tensor_inputs (`List`, *optional*):
+ The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+ will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+ `._callback_tensor_inputs` attribute of your pipeine class.
+
+ Examples:
Returns:
- `None` if cancelled by `is_cancelled_callback`,
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
- When returning a tuple, the first element is a list with the generated images, and the second element is a
- list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
- (nsfw) content, according to the `safety_checker`.
+ [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
+ [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+ `tuple`. When returning a tuple, the first element is a list with the generated images.
"""
+
+ callback = kwargs.pop("callback", None)
+ callback_steps = kwargs.pop("callback_steps", None)
+
+ if callback is not None:
+ deprecate(
+ "callback",
+ "1.0.0",
+ "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
+ )
+ if callback_steps is not None:
+ deprecate(
+ "callback_steps",
+ "1.0.0",
+ "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
+ )
+
# 0. Default height and width to unet
- height = height or self.unet.config.sample_size * self.vae_scale_factor
- width = width or self.unet.config.sample_size * self.vae_scale_factor
+ height = height or self.default_sample_size * self.vae_scale_factor
+ width = width or self.default_sample_size * self.vae_scale_factor
+
+ original_size = original_size or (height, width)
+ target_size = target_size or (height, width)
# 1. Check inputs. Raise error if not correct
- self.check_inputs(prompt, height, width, strength, callback_steps)
+ self.check_inputs(
+ prompt,
+ prompt_2,
+ height,
+ width,
+ strength,
+ callback_steps,
+ negative_prompt,
+ negative_prompt_2,
+ prompt_embeds,
+ negative_prompt_embeds,
+ pooled_prompt_embeds,
+ negative_pooled_prompt_embeds,
+ callback_on_step_end_tensor_inputs,
+ )
+
+ self._guidance_scale = guidance_scale
+ self._guidance_rescale = guidance_rescale
+ self._clip_skip = clip_skip
+ self._cross_attention_kwargs = cross_attention_kwargs
+ self._denoising_end = denoising_end
+ self._denoising_start = denoising_start
# 2. Define call parameters
- batch_size = 1 if isinstance(prompt, str) else len(prompt)
- # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
- # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
- # corresponds to doing no classifier free guidance.
- do_classifier_free_guidance = guidance_scale > 1.0
+ if prompt is not None and isinstance(prompt, str):
+ batch_size = 1
+ elif prompt is not None and isinstance(prompt, list):
+ batch_size = len(prompt)
+ else:
+ batch_size = prompt_embeds.shape[0]
+
+ device = self._execution_device
+
+ if ip_adapter_image is not None:
+ output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
+ image_embeds, negative_image_embeds = self.encode_image(
+ ip_adapter_image, device, num_images_per_prompt, output_hidden_state
+ )
+ if self.do_classifier_free_guidance:
+ image_embeds = torch.cat([negative_image_embeds, image_embeds])
# 3. Encode input prompt
- text_embeddings = self.encode_prompt(
- prompt,
- num_images_per_prompt,
- do_classifier_free_guidance,
- negative_prompt,
- max_embeddings_multiples,
+ (self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None)
+
+ negative_prompt = negative_prompt if negative_prompt is not None else ""
+
+ (
+ prompt_embeds,
+ negative_prompt_embeds,
+ pooled_prompt_embeds,
+ negative_pooled_prompt_embeds,
+ ) = get_weighted_text_embeddings_sdxl(
+ pipe=self,
+ prompt=prompt,
+ neg_prompt=negative_prompt,
+ num_images_per_prompt=num_images_per_prompt,
+ clip_skip=clip_skip,
)
- dtype = text_embeddings.dtype
+ dtype = prompt_embeds.dtype
- # 4. Preprocess image and mask
- if isinstance(image, PIL.Image.Image):
- image = preprocess_image(image)
+ if isinstance(image, Image.Image):
+ image = self.image_processor.preprocess(image, height=height, width=width)
if image is not None:
- image = image.astype(dtype)
- if isinstance(mask_image, PIL.Image.Image):
- mask_image = preprocess_mask(mask_image, self.vae_scale_factor)
+ image = image.to(device=self.device, dtype=dtype)
+
+ if isinstance(mask_image, Image.Image):
+ mask = self.mask_processor.preprocess(mask_image, height=height, width=width)
+ else:
+ mask = mask_image
if mask_image is not None:
- mask = mask_image.astype(dtype)
- mask = np.concatenate([mask] * batch_size * num_images_per_prompt)
+ mask = mask.to(device=self.device, dtype=dtype)
+
+ if masked_image_latents is not None:
+ masked_image = masked_image_latents
+ elif image.shape[1] == 4:
+ # if image is in latent space, we can't mask it
+ masked_image = None
+ else:
+ masked_image = image * (mask < 0.5)
else:
mask = None
- # 5. set timesteps
- self.scheduler.set_timesteps(num_inference_steps)
- timestep_dtype = next(
- (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
- )
- timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
- timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, image is None)
+ # 4. Prepare timesteps
+ def denoising_value_valid(dnv):
+ return isinstance(self.denoising_end, float) and 0 < dnv < 1
+
+ timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
+ if image is not None:
+ timesteps, num_inference_steps = self.get_timesteps(
+ num_inference_steps,
+ strength,
+ device,
+ denoising_start=self.denoising_start if denoising_value_valid else None,
+ )
+
+ # check that number of inference steps is not < 1 - as this doesn't make sense
+ if num_inference_steps < 1:
+ raise ValueError(
+ f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline"
+ f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline."
+ )
+
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+ is_strength_max = strength == 1.0
+ add_noise = True if self.denoising_start is None else False
- # 6. Prepare latent variables
- latents, init_latents_orig, noise = self.prepare_latents(
- image,
- latent_timestep,
- batch_size * num_images_per_prompt,
- height,
- width,
- dtype,
- generator,
- latents,
+ # 5. Prepare latent variables
+ num_channels_latents = self.vae.config.latent_channels
+ num_channels_unet = self.unet.config.in_channels
+ return_image_latents = num_channels_unet == 4
+
+ latents = self.prepare_latents(
+ image=image,
+ mask=mask,
+ width=width,
+ height=height,
+ num_channels_latents=num_channels_unet,
+ timestep=latent_timestep,
+ batch_size=batch_size,
+ num_images_per_prompt=num_images_per_prompt,
+ dtype=prompt_embeds.dtype,
+ device=device,
+ generator=generator,
+ add_noise=add_noise,
+ latents=latents,
+ is_strength_max=is_strength_max,
+ return_noise=True,
+ return_image_latents=return_image_latents,
)
- # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+ if mask is not None:
+ if return_image_latents:
+ latents, noise, image_latents = latents
+ else:
+ latents, noise = latents
+
+ # 5.1 Prepare mask latent variables
+ if mask is not None:
+ mask, masked_image_latents = self.prepare_mask_latents(
+ mask=mask,
+ masked_image=masked_image,
+ batch_size=batch_size * num_images_per_prompt,
+ height=height,
+ width=width,
+ dtype=prompt_embeds.dtype,
+ device=device,
+ generator=generator,
+ do_classifier_free_guidance=self.do_classifier_free_guidance,
+ )
+
+ # Check that sizes of mask, masked image and latents match
+ if num_channels_unet == 9:
+ # default case for runwayml/stable-diffusion-inpainting
+ num_channels_mask = mask.shape[1]
+ num_channels_masked_image = masked_image_latents.shape[1]
+ if num_channels_latents + num_channels_mask + num_channels_masked_image != num_channels_unet:
+ raise ValueError(
+ f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
+ f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
+ f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
+ f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
+ " `pipeline.unet` or your `mask_image` or `image` input."
+ )
+ elif num_channels_unet != 4:
+ raise ValueError(
+ f"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}."
+ )
+
+ # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
- # 8. Denoising loop
- for i, t in enumerate(self.progress_bar(timesteps)):
- # expand the latents if we are doing classifier free guidance
- latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
- latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
- latent_model_input = latent_model_input.numpy()
-
- # predict the noise residual
- noise_pred = self.unet(
- sample=latent_model_input,
- timestep=np.array([t], dtype=timestep_dtype),
- encoder_hidden_states=text_embeddings,
- )
- noise_pred = noise_pred[0]
+ # 6.1 Add image embeds for IP-Adapter
+ added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else {}
- # perform guidance
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
- noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+ height, width = latents.shape[-2:]
+ height = height * self.vae_scale_factor
+ width = width * self.vae_scale_factor
- # compute the previous noisy sample x_t -> x_t-1
- scheduler_output = self.scheduler.step(
- torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
- )
- latents = scheduler_output.prev_sample.numpy()
+ original_size = original_size or (height, width)
+ target_size = target_size or (height, width)
- if mask is not None:
- # masking
- init_latents_proper = self.scheduler.add_noise(
- torch.from_numpy(init_latents_orig),
- torch.from_numpy(noise),
- t,
- ).numpy()
- latents = (init_latents_proper * mask) + (latents * (1 - mask))
+ # 7. Prepare added time ids & embeddings
+ add_text_embeds = pooled_prompt_embeds
+ add_time_ids = self._get_add_time_ids(
+ original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
+ )
- # call the callback, if provided
- if i % callback_steps == 0:
- if callback is not None:
- step_idx = i // getattr(self.scheduler, "order", 1)
- callback(step_idx, t, latents)
- if is_cancelled_callback is not None and is_cancelled_callback():
- return None
+ if self.do_classifier_free_guidance:
+ prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+ add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
+ add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
+
+ prompt_embeds = prompt_embeds.to(device)
+ add_text_embeds = add_text_embeds.to(device)
+ add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
+
+ num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+ # 7.1 Apply denoising_end
+ if (
+ self.denoising_end is not None
+ and self.denoising_start is not None
+ and denoising_value_valid(self.denoising_end)
+ and denoising_value_valid(self.denoising_start)
+ and self.denoising_start >= self.denoising_end
+ ):
+ raise ValueError(
+ f"`denoising_start`: {self.denoising_start} cannot be larger than or equal to `denoising_end`: "
+ + f" {self.denoising_end} when using type float."
+ )
+ elif self.denoising_end is not None and denoising_value_valid(self.denoising_end):
+ discrete_timestep_cutoff = int(
+ round(
+ self.scheduler.config.num_train_timesteps
+ - (self.denoising_end * self.scheduler.config.num_train_timesteps)
+ )
+ )
+ num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
+ timesteps = timesteps[:num_inference_steps]
+
+ # 8. Optionally get Guidance Scale Embedding
+ timestep_cond = None
+ if self.unet.config.time_cond_proj_dim is not None:
+ guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
+ timestep_cond = self.get_guidance_scale_embedding(
+ guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+ ).to(device=device, dtype=latents.dtype)
+
+ self._num_timesteps = len(timesteps)
+
+ # 9. Denoising loop
+ with self.progress_bar(total=num_inference_steps) as progress_bar:
+ for i, t in enumerate(timesteps):
+ # expand the latents if we are doing classifier free guidance
+ latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+
+ latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+ if mask is not None and num_channels_unet == 9:
+ latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
+
+ # predict the noise residual
+ added_cond_kwargs.update({"text_embeds": add_text_embeds, "time_ids": add_time_ids})
+ noise_pred = self.unet(
+ latent_model_input,
+ t,
+ encoder_hidden_states=prompt_embeds,
+ timestep_cond=timestep_cond,
+ cross_attention_kwargs=self.cross_attention_kwargs,
+ added_cond_kwargs=added_cond_kwargs,
+ return_dict=False,
+ )[0]
+
+ # perform guidance
+ if self.do_classifier_free_guidance:
+ noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+ noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+ if self.do_classifier_free_guidance and guidance_rescale > 0.0:
+ # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+ noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+ # compute the previous noisy sample x_t -> x_t-1
+ latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+ if mask is not None and num_channels_unet == 4:
+ init_latents_proper = image_latents
+
+ if self.do_classifier_free_guidance:
+ init_mask, _ = mask.chunk(2)
+ else:
+ init_mask = mask
+
+ if i < len(timesteps) - 1:
+ noise_timestep = timesteps[i + 1]
+ init_latents_proper = self.scheduler.add_noise(
+ init_latents_proper, noise, torch.tensor([noise_timestep])
+ )
+
+ latents = (1 - init_mask) * init_latents_proper + init_mask * latents
+
+ if callback_on_step_end is not None:
+ callback_kwargs = {}
+ for k in callback_on_step_end_tensor_inputs:
+ callback_kwargs[k] = locals()[k]
+ callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+ latents = callback_outputs.pop("latents", latents)
+ prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+ negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+ add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
+ negative_pooled_prompt_embeds = callback_outputs.pop(
+ "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+ )
+ add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
+
+ # call the callback, if provided
+ if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+ progress_bar.update()
+ if callback is not None and i % callback_steps == 0:
+ step_idx = i // getattr(self.scheduler, "order", 1)
+ callback(step_idx, t, latents)
+
+ if not output_type == "latent":
+ # make sure the VAE is in float32 mode, as it overflows in float16
+ needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+
+ if needs_upcasting:
+ self.upcast_vae()
+ latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+
+ image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+
+ # cast back to fp16 if needed
+ if needs_upcasting:
+ self.vae.to(dtype=torch.float16)
+ else:
+ image = latents
+ return StableDiffusionXLPipelineOutput(images=image)
- # 9. Post-processing
- image = self.decode_latents(latents)
+ # apply watermark if available
+ if self.watermark is not None:
+ image = self.watermark.apply_watermark(image)
- # 10. Run safety checker
- image, has_nsfw_concept = self.run_safety_checker(image)
+ image = self.image_processor.postprocess(image, output_type=output_type)
- # 11. Convert to PIL
- if output_type == "pil":
- image = self.numpy_to_pil(image)
+ # Offload last model to CPU
+ if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+ self.final_offload_hook.offload()
if not return_dict:
- return image, has_nsfw_concept
+ return (image,)
- return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+ return StableDiffusionXLPipelineOutput(images=image)
def text2img(
self,
- prompt: Union[str, List[str]],
- negative_prompt: Optional[Union[str, List[str]]] = None,
- height: int = 512,
- width: int = 512,
+ prompt: str = None,
+ prompt_2: Optional[str] = None,
+ height: Optional[int] = None,
+ width: Optional[int] = None,
num_inference_steps: int = 50,
- guidance_scale: float = 7.5,
+ timesteps: List[int] = None,
+ denoising_start: Optional[float] = None,
+ denoising_end: Optional[float] = None,
+ guidance_scale: float = 5.0,
+ negative_prompt: Optional[str] = None,
+ negative_prompt_2: Optional[str] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
- generator: Optional[torch.Generator] = None,
- latents: Optional[np.ndarray] = None,
- max_embeddings_multiples: Optional[int] = 3,
+ generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+ latents: Optional[torch.FloatTensor] = None,
+ ip_adapter_image: Optional[PipelineImageInput] = None,
+ prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+ pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
- callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
- callback_steps: int = 1,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ guidance_rescale: float = 0.0,
+ original_size: Optional[Tuple[int, int]] = None,
+ crops_coords_top_left: Tuple[int, int] = (0, 0),
+ target_size: Optional[Tuple[int, int]] = None,
+ clip_skip: Optional[int] = None,
+ callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+ callback_on_step_end_tensor_inputs: List[str] = ["latents"],
**kwargs,
):
r"""
- Function for text-to-image generation.
- Args:
- prompt (`str` or `List[str]`):
- The prompt or prompts to guide the image generation.
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
- if `guidance_scale` is less than `1`).
- height (`int`, *optional*, defaults to 512):
- The height in pixels of the generated image.
- width (`int`, *optional*, defaults to 512):
- The width in pixels of the generated image.
- num_inference_steps (`int`, *optional*, defaults to 50):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference.
- guidance_scale (`float`, *optional*, defaults to 7.5):
- Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
- `guidance_scale` is defined as `w` of equation 2. of [Imagen
- Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
- 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
- usually at the expense of lower image quality.
- num_images_per_prompt (`int`, *optional*, defaults to 1):
- The number of images to generate per prompt.
- eta (`float`, *optional*, defaults to 0.0):
- Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
- [`schedulers.DDIMScheduler`], will be ignored for others.
- generator (`torch.Generator`, *optional*):
- A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
- deterministic.
- latents (`np.ndarray`, *optional*):
- Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
- generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
- tensor will ge generated by sampling using the supplied random `generator`.
- max_embeddings_multiples (`int`, *optional*, defaults to `3`):
- The max multiple length of prompt embeddings compared to the max output length of text encoder.
- output_type (`str`, *optional*, defaults to `"pil"`):
- The output format of the generate image. Choose between
- [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
- plain tuple.
- callback (`Callable`, *optional*):
- A function that will be called every `callback_steps` steps during inference. The function will be
- called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
- callback_steps (`int`, *optional*, defaults to 1):
- The frequency at which the `callback` function will be called. If not specified, the callback will be
- called at every step.
- Returns:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
- When returning a tuple, the first element is a list with the generated images, and the second element is a
- list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
- (nsfw) content, according to the `safety_checker`.
+ Function invoked when calling pipeline for text-to-image.
+
+ Refer to the documentation of the `__call__` method for parameter descriptions.
"""
return self.__call__(
prompt=prompt,
- negative_prompt=negative_prompt,
+ prompt_2=prompt_2,
height=height,
width=width,
num_inference_steps=num_inference_steps,
+ timesteps=timesteps,
+ denoising_start=denoising_start,
+ denoising_end=denoising_end,
guidance_scale=guidance_scale,
+ negative_prompt=negative_prompt,
+ negative_prompt_2=negative_prompt_2,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
latents=latents,
- max_embeddings_multiples=max_embeddings_multiples,
+ ip_adapter_image=ip_adapter_image,
+ prompt_embeds=prompt_embeds,
+ negative_prompt_embeds=negative_prompt_embeds,
+ pooled_prompt_embeds=pooled_prompt_embeds,
+ negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
output_type=output_type,
return_dict=return_dict,
- callback=callback,
- callback_steps=callback_steps,
+ cross_attention_kwargs=cross_attention_kwargs,
+ guidance_rescale=guidance_rescale,
+ original_size=original_size,
+ crops_coords_top_left=crops_coords_top_left,
+ target_size=target_size,
+ clip_skip=clip_skip,
+ callback_on_step_end=callback_on_step_end,
+ callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
**kwargs,
)
def img2img(
self,
- image: Union[np.ndarray, PIL.Image.Image],
- prompt: Union[str, List[str]],
- negative_prompt: Optional[Union[str, List[str]]] = None,
+ prompt: str = None,
+ prompt_2: Optional[str] = None,
+ image: Optional[PipelineImageInput] = None,
+ height: Optional[int] = None,
+ width: Optional[int] = None,
strength: float = 0.8,
- num_inference_steps: Optional[int] = 50,
- guidance_scale: Optional[float] = 7.5,
+ num_inference_steps: int = 50,
+ timesteps: List[int] = None,
+ denoising_start: Optional[float] = None,
+ denoising_end: Optional[float] = None,
+ guidance_scale: float = 5.0,
+ negative_prompt: Optional[str] = None,
+ negative_prompt_2: Optional[str] = None,
num_images_per_prompt: Optional[int] = 1,
- eta: Optional[float] = 0.0,
- generator: Optional[torch.Generator] = None,
- max_embeddings_multiples: Optional[int] = 3,
+ eta: float = 0.0,
+ generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+ latents: Optional[torch.FloatTensor] = None,
+ ip_adapter_image: Optional[PipelineImageInput] = None,
+ prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+ pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
- callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
- callback_steps: int = 1,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ guidance_rescale: float = 0.0,
+ original_size: Optional[Tuple[int, int]] = None,
+ crops_coords_top_left: Tuple[int, int] = (0, 0),
+ target_size: Optional[Tuple[int, int]] = None,
+ clip_skip: Optional[int] = None,
+ callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+ callback_on_step_end_tensor_inputs: List[str] = ["latents"],
**kwargs,
):
r"""
- Function for image-to-image generation.
- Args:
- image (`np.ndarray` or `PIL.Image.Image`):
- `Image`, or ndarray representing an image batch, that will be used as the starting point for the
- process.
- prompt (`str` or `List[str]`):
- The prompt or prompts to guide the image generation.
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
- if `guidance_scale` is less than `1`).
- strength (`float`, *optional*, defaults to 0.8):
- Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1.
- `image` will be used as a starting point, adding more noise to it the larger the `strength`. The
- number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added
- noise will be maximum and the denoising process will run for the full number of iterations specified in
- `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
- num_inference_steps (`int`, *optional*, defaults to 50):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference. This parameter will be modulated by `strength`.
- guidance_scale (`float`, *optional*, defaults to 7.5):
- Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
- `guidance_scale` is defined as `w` of equation 2. of [Imagen
- Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
- 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
- usually at the expense of lower image quality.
- num_images_per_prompt (`int`, *optional*, defaults to 1):
- The number of images to generate per prompt.
- eta (`float`, *optional*, defaults to 0.0):
- Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
- [`schedulers.DDIMScheduler`], will be ignored for others.
- generator (`torch.Generator`, *optional*):
- A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
- deterministic.
- max_embeddings_multiples (`int`, *optional*, defaults to `3`):
- The max multiple length of prompt embeddings compared to the max output length of text encoder.
- output_type (`str`, *optional*, defaults to `"pil"`):
- The output format of the generate image. Choose between
- [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
- plain tuple.
- callback (`Callable`, *optional*):
- A function that will be called every `callback_steps` steps during inference. The function will be
- called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
- callback_steps (`int`, *optional*, defaults to 1):
- The frequency at which the `callback` function will be called. If not specified, the callback will be
- called at every step.
- Returns:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
- When returning a tuple, the first element is a list with the generated images, and the second element is a
- list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
- (nsfw) content, according to the `safety_checker`.
+ Function invoked when calling pipeline for image-to-image.
+
+ Refer to the documentation of the `__call__` method for parameter descriptions.
"""
return self.__call__(
prompt=prompt,
- negative_prompt=negative_prompt,
+ prompt_2=prompt_2,
image=image,
+ height=height,
+ width=width,
+ strength=strength,
num_inference_steps=num_inference_steps,
+ timesteps=timesteps,
+ denoising_start=denoising_start,
+ denoising_end=denoising_end,
guidance_scale=guidance_scale,
- strength=strength,
+ negative_prompt=negative_prompt,
+ negative_prompt_2=negative_prompt_2,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
- max_embeddings_multiples=max_embeddings_multiples,
+ latents=latents,
+ ip_adapter_image=ip_adapter_image,
+ prompt_embeds=prompt_embeds,
+ negative_prompt_embeds=negative_prompt_embeds,
+ pooled_prompt_embeds=pooled_prompt_embeds,
+ negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
output_type=output_type,
return_dict=return_dict,
- callback=callback,
- callback_steps=callback_steps,
+ cross_attention_kwargs=cross_attention_kwargs,
+ guidance_rescale=guidance_rescale,
+ original_size=original_size,
+ crops_coords_top_left=crops_coords_top_left,
+ target_size=target_size,
+ clip_skip=clip_skip,
+ callback_on_step_end=callback_on_step_end,
+ callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
**kwargs,
)
def inpaint(
self,
- image: Union[np.ndarray, PIL.Image.Image],
- mask_image: Union[np.ndarray, PIL.Image.Image],
- prompt: Union[str, List[str]],
- negative_prompt: Optional[Union[str, List[str]]] = None,
+ prompt: str = None,
+ prompt_2: Optional[str] = None,
+ image: Optional[PipelineImageInput] = None,
+ mask_image: Optional[PipelineImageInput] = None,
+ masked_image_latents: Optional[torch.FloatTensor] = None,
+ height: Optional[int] = None,
+ width: Optional[int] = None,
strength: float = 0.8,
- num_inference_steps: Optional[int] = 50,
- guidance_scale: Optional[float] = 7.5,
+ num_inference_steps: int = 50,
+ timesteps: List[int] = None,
+ denoising_start: Optional[float] = None,
+ denoising_end: Optional[float] = None,
+ guidance_scale: float = 5.0,
+ negative_prompt: Optional[str] = None,
+ negative_prompt_2: Optional[str] = None,
num_images_per_prompt: Optional[int] = 1,
- eta: Optional[float] = 0.0,
- generator: Optional[torch.Generator] = None,
- max_embeddings_multiples: Optional[int] = 3,
+ eta: float = 0.0,
+ generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+ latents: Optional[torch.FloatTensor] = None,
+ ip_adapter_image: Optional[PipelineImageInput] = None,
+ prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+ pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
- callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
- callback_steps: int = 1,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ guidance_rescale: float = 0.0,
+ original_size: Optional[Tuple[int, int]] = None,
+ crops_coords_top_left: Tuple[int, int] = (0, 0),
+ target_size: Optional[Tuple[int, int]] = None,
+ clip_skip: Optional[int] = None,
+ callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+ callback_on_step_end_tensor_inputs: List[str] = ["latents"],
**kwargs,
):
r"""
- Function for inpaint.
- Args:
- image (`np.ndarray` or `PIL.Image.Image`):
- `Image`, or tensor representing an image batch, that will be used as the starting point for the
- process. This is the image whose masked region will be inpainted.
- mask_image (`np.ndarray` or `PIL.Image.Image`):
- `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
- replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
- PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
- contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
- prompt (`str` or `List[str]`):
- The prompt or prompts to guide the image generation.
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
- if `guidance_scale` is less than `1`).
- strength (`float`, *optional*, defaults to 0.8):
- Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
- is 1, the denoising process will be run on the masked area for the full number of iterations specified
- in `num_inference_steps`. `image` will be used as a reference for the masked area, adding more
- noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
- num_inference_steps (`int`, *optional*, defaults to 50):
- The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
- the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
- guidance_scale (`float`, *optional*, defaults to 7.5):
- Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
- `guidance_scale` is defined as `w` of equation 2. of [Imagen
- Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
- 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
- usually at the expense of lower image quality.
- num_images_per_prompt (`int`, *optional*, defaults to 1):
- The number of images to generate per prompt.
- eta (`float`, *optional*, defaults to 0.0):
- Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
- [`schedulers.DDIMScheduler`], will be ignored for others.
- generator (`torch.Generator`, *optional*):
- A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
- deterministic.
- max_embeddings_multiples (`int`, *optional*, defaults to `3`):
- The max multiple length of prompt embeddings compared to the max output length of text encoder.
- output_type (`str`, *optional*, defaults to `"pil"`):
- The output format of the generate image. Choose between
- [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
- plain tuple.
- callback (`Callable`, *optional*):
- A function that will be called every `callback_steps` steps during inference. The function will be
- called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
- callback_steps (`int`, *optional*, defaults to 1):
- The frequency at which the `callback` function will be called. If not specified, the callback will be
- called at every step.
- Returns:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
- When returning a tuple, the first element is a list with the generated images, and the second element is a
- list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
- (nsfw) content, according to the `safety_checker`.
+ Function invoked when calling pipeline for inpainting.
+
+ Refer to the documentation of the `__call__` method for parameter descriptions.
"""
return self.__call__(
prompt=prompt,
- negative_prompt=negative_prompt,
+ prompt_2=prompt_2,
image=image,
mask_image=mask_image,
+ masked_image_latents=masked_image_latents,
+ height=height,
+ width=width,
+ strength=strength,
num_inference_steps=num_inference_steps,
+ timesteps=timesteps,
+ denoising_start=denoising_start,
+ denoising_end=denoising_end,
guidance_scale=guidance_scale,
- strength=strength,
+ negative_prompt=negative_prompt,
+ negative_prompt_2=negative_prompt_2,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
- max_embeddings_multiples=max_embeddings_multiples,
+ latents=latents,
+ ip_adapter_image=ip_adapter_image,
+ prompt_embeds=prompt_embeds,
+ negative_prompt_embeds=negative_prompt_embeds,
+ pooled_prompt_embeds=pooled_prompt_embeds,
+ negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
output_type=output_type,
return_dict=return_dict,
- callback=callback,
- callback_steps=callback_steps,
+ cross_attention_kwargs=cross_attention_kwargs,
+ guidance_rescale=guidance_rescale,
+ original_size=original_size,
+ crops_coords_top_left=crops_coords_top_left,
+ target_size=target_size,
+ clip_skip=clip_skip,
+ callback_on_step_end=callback_on_step_end,
+ callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
**kwargs,
- )
\ No newline at end of file
+ )
+
+ # Overrride to properly handle the loading and unloading of the additional text encoder.
+ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
+ # We could have accessed the unet config from `lora_state_dict()` too. We pass
+ # it here explicitly to be able to tell that it's coming from an SDXL
+ # pipeline.
+ state_dict, network_alphas = self.lora_state_dict(
+ pretrained_model_name_or_path_or_dict,
+ unet_config=self.unet.config,
+ **kwargs,
+ )
+ self.load_lora_into_unet(state_dict, network_alphas=network_alphas, unet=self.unet)
+
+ text_encoder_state_dict = {k: v for k, v in state_dict.items() if "text_encoder." in k}
+ if len(text_encoder_state_dict) > 0:
+ self.load_lora_into_text_encoder(
+ text_encoder_state_dict,
+ network_alphas=network_alphas,
+ text_encoder=self.text_encoder,
+ prefix="text_encoder",
+ lora_scale=self.lora_scale,
+ )
+
+ text_encoder_2_state_dict = {k: v for k, v in state_dict.items() if "text_encoder_2." in k}
+ if len(text_encoder_2_state_dict) > 0:
+ self.load_lora_into_text_encoder(
+ text_encoder_2_state_dict,
+ network_alphas=network_alphas,
+ text_encoder=self.text_encoder_2,
+ prefix="text_encoder_2",
+ lora_scale=self.lora_scale,
+ )
+
+ @classmethod
+ def save_lora_weights(
+ self,
+ save_directory: Union[str, os.PathLike],
+ unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+ text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+ text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+ is_main_process: bool = True,
+ weight_name: str = None,
+ save_function: Callable = None,
+ safe_serialization: bool = False,
+ ):
+ state_dict = {}
+
+ def pack_weights(layers, prefix):
+ layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers
+ layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()}
+ return layers_state_dict
+
+ state_dict.update(pack_weights(unet_lora_layers, "unet"))
+
+ if text_encoder_lora_layers and text_encoder_2_lora_layers:
+ state_dict.update(pack_weights(text_encoder_lora_layers, "text_encoder"))
+ state_dict.update(pack_weights(text_encoder_2_lora_layers, "text_encoder_2"))
+
+ self.write_lora_layers(
+ state_dict=state_dict,
+ save_directory=save_directory,
+ is_main_process=is_main_process,
+ weight_name=weight_name,
+ save_function=save_function,
+ safe_serialization=safe_serialization,
+ )
+
+ def _remove_text_encoder_monkey_patch(self):
+ self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder)
+ self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder_2)
\ No newline at end of file